Compare Apple to Apples

You are likely asked for next years seed order many times before harvest even begins. In that case, one of the first decisions you probably will make using your yield data is which numbers to plant the following year.

Seed plots serve as away to visually see new and current hybrids, making you knee-deep in plot results and “percent of wins” data. But in most cases, your own results and experiences will trump plot books and seed guides.

The basic idea of a plot is to test the genetic potential of a hybrid or variety in a growing environment where other variables are controlled and non-yield-limiting. The results are uniform, Well-drained, fertile plots that frequently don’t resemble the diverse environments that you farm. Because of that, your own data is a great starting place.

As you spend time analyzing your data, you will start to understand that sometimes tables of different varieties are”apples to oranges” comparisons, creating the need to dig deeper. For example, by looking at this “Yield by hybrid” chart, a grower may think the red hybrid is the clear winner. However, if you dig deeper into the data, such as analyzing yield by hybrid by soil type, like in the bar chart, you will notice the red hybrid was not the best when it was in the Alda soil type.

There are other factors you can find when digging though data to make fair comparisons. Consider why one hybrid did better than another. The reality is that some hybrids get the benefit of being in the best possible situation on the best ground, and some get the worst. Strive to identify and more accurately place your genetics.

For example, place the racehorse numbers in the ideal environment and the defensive numbers in less than ideal environments.

The key is to never stop digging for the answer to “why?” It is easy  in all data analysis to have “apples to oranges” comparisons and take data at surface value, but the key to good analysis is to keep digging deeper to get fair comparisons, thus creating the most educated and profitable agronomic decisions.

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